{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vectorized Backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:30.483283Z",
     "start_time": "2020-06-17T20:19:30.481083Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.002447Z",
     "start_time": "2020-06-17T20:19:30.484763Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from time import time\n",
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.005906Z",
     "start_time": "2020-06-17T20:19:31.003448Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Return Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.016323Z",
     "start_time": "2020-06-17T20:19:31.006932Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_DIR = Path('..', 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.065529Z",
     "start_time": "2020-06-17T20:19:31.017307Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 190451 entries, ('AAL', Timestamp('2014-12-09 00:00:00')) to ('YUM', Timestamp('2017-11-30 00:00:00'))\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   predicted  74054 non-null   float64\n",
      " 1   open       190451 non-null  float64\n",
      " 2   high       190451 non-null  float64\n",
      " 3   low        190451 non-null  float64\n",
      " 4   close      190451 non-null  float64\n",
      " 5   volume     190451 non-null  float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 10.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_hdf('00_data/backtest.h5', 'data')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SP500 Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.149785Z",
     "start_time": "2020-06-17T20:19:31.066496Z"
    }
   },
   "outputs": [],
   "source": [
    "sp500 = web.DataReader('SP500', 'fred', '2014', '2018').pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.154957Z",
     "start_time": "2020-06-17T20:19:31.151155Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1044 entries, 2014-01-01 to 2018-01-01\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   SP500   1042 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 16.3 KB\n"
     ]
    }
   ],
   "source": [
    "sp500.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Forward Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.204895Z",
     "start_time": "2020-06-17T20:19:31.155977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 751 entries, 2014-12-09 to 2017-11-30\n",
      "Columns: 257 entries, AAL to YUM\n",
      "dtypes: float64(257)\n",
      "memory usage: 1.5 MB\n"
     ]
    }
   ],
   "source": [
    "daily_returns = data.open.unstack('ticker').sort_index().pct_change()\n",
    "daily_returns.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.208072Z",
     "start_time": "2020-06-17T20:19:31.205865Z"
    }
   },
   "outputs": [],
   "source": [
    "fwd_returns = daily_returns.shift(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.282492Z",
     "start_time": "2020-06-17T20:19:31.208996Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 751 entries, 2014-12-09 to 2017-04-14\n",
      "Columns: 257 entries, AAL to YUM\n",
      "dtypes: float64(257)\n",
      "memory usage: 1.5 MB\n"
     ]
    }
   ],
   "source": [
    "predictions = data.predicted.unstack('ticker')\n",
    "predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.285384Z",
     "start_time": "2020-06-17T20:19:31.283647Z"
    }
   },
   "outputs": [],
   "source": [
    "N_LONG = N_SHORT = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.309345Z",
     "start_time": "2020-06-17T20:19:31.286229Z"
    }
   },
   "outputs": [],
   "source": [
    "long_signals = ((predictions\n",
    "                .where(predictions > 0)\n",
    "                .rank(axis=1, ascending=False) > N_LONG)\n",
    "                .astype(int))\n",
    "short_signals = ((predictions\n",
    "                  .where(predictions < 0)\n",
    "                  .rank(axis=1) > N_SHORT)\n",
    "                 .astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Portfolio Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.322346Z",
     "start_time": "2020-06-17T20:19:31.310454Z"
    }
   },
   "outputs": [],
   "source": [
    "long_returns = long_signals.mul(fwd_returns).mean(axis=1)\n",
    "short_returns = short_signals.mul(-fwd_returns).mean(axis=1)\n",
    "strategy = long_returns.add(short_returns).to_frame('Strategy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.823702Z",
     "start_time": "2020-06-17T20:19:31.323207Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(14,5))\n",
    "strategy.join(sp500).add(1).cumprod().sub(1).plot(ax=axes[0], title='Cumulative Return')\n",
    "sns.distplot(strategy.dropna(), ax=axes[1], hist=False, label='Strategy')\n",
    "sns.distplot(sp500, ax=axes[1], hist=False, label='SP500')\n",
    "axes[1].set_title('Daily Standard Deviation')\n",
    "axes[0].yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y))) \n",
    "axes[1].xaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y))) \n",
    "sns.despine()\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.828261Z",
     "start_time": "2020-06-17T20:19:31.824576Z"
    }
   },
   "outputs": [],
   "source": [
    "res = strategy.join(sp500).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.843867Z",
     "start_time": "2020-06-17T20:19:31.829113Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Strategy    0.001981\n",
       "SP500       0.007923\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T20:19:31.859983Z",
     "start_time": "2020-06-17T20:19:31.845286Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Strategy</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Strategy</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.1184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP500</th>\n",
       "      <td>-0.1184</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Strategy   SP500\n",
       "Strategy    1.0000 -0.1184\n",
       "SP500      -0.1184  1.0000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.corr()"
   ]
  }
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